scholarly journals Improved Adaptive Neuro-Fuzzy Inference System Based on Particle Swarm Optimization Algorithm for Predicting Labor Productivity

2021 ◽  
Author(s):  
Nehal Elshaboury ◽  
Abobakr Al-Sakkaf ◽  
Ghasan Alfalah ◽  
Eslam Mohammed Abdelkader
Keyword(s):  
Particle Swarm Optimization ◽  
Labor Productivity ◽  
Optimization Algorithm ◽  
Fuzzy Inference System ◽  
Particle Swarm Optimization Algorithm ◽  
Fuzzy Inference ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Inference System ◽  
Neuro Fuzzy

Prediction of tool wear width size and optimization of cutting parameters in milling process using novel ANFIS-PSO method

2020 ◽  
pp. 095440542093526
Author(s):  
Longhua Xu ◽  
Chuanzhen Huang ◽  
Chengwu Li ◽  
Jun Wang ◽  
Hanlian Liu ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Tool Wear ◽  
Optimization Algorithm ◽  
Fuzzy Inference System ◽  
Random Vibration ◽  
Particle Swarm Optimization Algorithm ◽  
Fuzzy Inference ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Inference System

In the process of intelligent manufacturing, appropriate learning algorithm and intelligent model are necessary. In this work, a novel learning algorithm named random vibration and cross particle swarm optimization algorithm was proposed. The proposed algorithm is used for the prediction and optimization of machining process. Tool wear is an important factor that affects the machined surface quality during machining process, so it is necessary to find qualified tool wear prediction model and obtain the best combination of machining parameters to prolong tool life. In this study, the adaptive network–based fuzzy inference system was established to predict the tool wear width size. The random vibration and cross particle swarm optimization algorithm was tested using benchmark functions, and the results showed that random vibration and cross particle swarm optimization algorithm is able to find global optimum. Compared with the adaptive network–based fuzzy inference system trained by particle swarm optimization algorithm and adaptive network–based fuzzy inference system trained by differential evolution models, the results showed that the adaptive network–based fuzzy inference system trained by random vibration and cross particle swarm optimization algorithm can give a more accurate predicted value for offline prediction of the tool wear width size. In order to obtain the best combinations of cutting parameters under different removal area, the multi-objective optimization based on random vibration and cross particle swarm optimization algorithm was established. The optimized cutting parameters were verified and could be accepted to prolong tool life and improve machining efficiency.

scholarly journals A OPTIMIZATION TUNED ADAPTIVE NEURO-FUZZY INFERENCE SYSTEM FOR DAM DEFORMATION PREDICTION

2020 ◽  
Vol XLII-3/W10 ◽  
pp. 1207-1213
Author(s):  
X. Y. Zhang ◽  
B. Wei
Keyword(s):  
Particle Swarm Optimization ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Particle Swarm ◽  
Local Optimum ◽  
Swarm Optimization ◽  
Deformation Prediction ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Dam Deformation

Abstract. The performance and stability of Adaptive Neuro-Fuzzy Inference System (ANFIS) depend on its network structure and preset parameter selection, and Particle Swarm Optimization-ANFIS (PSO-ANFIS) easily falls into the local optimum and is imprecise. A novel ANFIS algorithm tuned by Chaotic Particle Swarm Optimization (CPSO-ANFIS) is proposed to solve these problems. A chaotic ergodic algorithm is first used to improve the PSO and obtain a CPSO algorithm, and then the CPSO is used to optimize the parameters of ANFIS to avoid falling into the local optimum and improve the performance of ANFIS. Based on the deformation data from the Xiaolangdi Dam in China, three neural network algorithms, ANFIS, PSO-ANFIS, and CPSO-ANFIS, are used to establish the dam deformation prediction models after data preparation and selection of influencing factors for the dam deformation. The results are compared using evaluation indicators that show that CPSO-ANFIS is more accurate and stable than ANFIS and PSO-ANFIS both in predictive ability and in predicted results.

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